Across day/night and weekday/weekend partitions, our word embedding model uncovered a hidden temporal cycle of morality within r/confession; one where institutional/structured time (day, weekday) clustered around bureaucratic terms like "documents", "exams", "appointments", intimate/private time (night) clustered around relationship/emotional terms like "betrayal", "intimacy", and "tension", while leisure time (weekend) clustered around terms like "video games", "party", and "beer". This macro pattern is not just visible in the computational model, but its also prevalant close reading. The weekday "appointment" confession, for instance, narrates harm through adherence to bureaucratic norms of quotas and deadline. On another hand, the night "betrayal" confession details an event of emotional trauma coming from close relationships. Meanwhile, both weekend confessions detail casual mischievous acts. Computationally, co-occurrence norms gave a map of likely meanings that helped select representative posts and notice recurring ethical frames.
Bag-of-words and embeddings flatten who speaks, to whom, and how. TF-IDF ignores word order and negation ("didn't mean to…" vs "meant to..."), and lemmatization plus stop-word removal can strip stance markers ("should," "have to," "can't") that carry moral weight. Word2Vec learns proximity, not causality; a word close to "night" is not necessarily "about" night so much as co-used with it. The global vectorizer also privileges frequent terms, muting the long-tail idioms that mark smaller subcultures. Finally, event time and posting time can diverge ("last night," "ten years ago"), so our temporal binning strategy risks conflating deictic narratives with metadata time.
Throughout our research, we used the assistance of ChatGPT-5 primarily in generating boilerplate code for computation. Portions of code where AI assistance was used are clearly marked in the Jupyter Notebook here at the bottom of the Tools and Libraries page. Code generation was limited to the temporal partitioning step of computation. The impact of the code generation in these section on our final results are minimal to none. AI assistance in the creation of this website is limited to the creation of the html table in the same Tools and Libraries page, as well as general formatting guidance.
Throughout our research, there were several assumptions made and other inherient biases that could have greatly affected our findings.
Timezone and Language: We centered our analysis on English language posts as well as assuming that every post was made by someone residing in an American time zone, making EST/EDT (UTC-5/4) our centre.
Self-censorship: Confessions that remain visible have already been through significant self-censorship before being posted online. Furthermore, some extremely sensitive confessions may not have survived the self-censorship filter and thus are unavailable for analysis.
Moral framing: Our supposed binary relations formed from day/night and weekday/weekday partitions are useful lenses for generalized analysis but may have been reductive as it is still possible for daytime posts to carry immense emotional weight or weekday posts to be about intimate relationships.